MiMeNet

MiMeNet models relationships between microbiome compositions and metabolomic profiles using neural networks to predict metabolite abundances and elucidate microbe–metabolite interactions relevant to disease pathogenesis.

Key Features:

• Predictive Modeling: Predicts metabolite abundances from microbiome data using neural networks with hyper-parameter tuning.
• Cross-validation: Evaluates models using ten iterations of 10-fold cross-validation across three paired microbiome-metabolome datasets.
• Performance Metrics: Demonstrates improved Spearman correlation coefficients (SCC) versus linear models with SCC ranges of 0.108–0.309, 0.276–0.457, and -0.272–0.264 across datasets.
• Identification of Metabolites: Increases the number of well-predicted metabolites from 198 to 366, 104 to 143, and 4 to 29 in the respective datasets.

Scientific Applications:

• Disease Pathogenesis Insights: Uncovers microbe–metabolite interactions that provide insights into metabolic dysregulation associated with diseases.
• Hypothesis Generation: Facilitates generation of hypotheses at the intersection of microbiome and metabolomics research.
• Network Analysis: Groups microbes and metabolites by similar interaction patterns and can reveal uncharacterized metabolites via a "Guilt by Association" approach.

Methodology:

Employs neural network models to capture non-linear relationships, uses hyper-parameter tuning to optimize performance, and applies ten iterations of 10-fold cross-validation across three paired microbiome–metabolome datasets.